Over the past decade, localization systems fusing inertial data from an inertial measurement unit (IMU) with visual observations from a camera [i.e., vision-aided inertial navigation systems (VINS)] have become a popular choice for GPS-denied navigation (e.g., navigation indoors or in space). Among the methods employed for tracking the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a device within GPS-denied environments, vision-aided inertial navigation is one of the most prominent, primarily due to its high precision and low cost. During the past decade, VINS have been successfully applied to spacecraft, automotive, and personal localization, demonstrating real-time performance.
In general, each VINS implements an estimator that fuses data from one or more cameras and an Inertial Measurement Unit (IMU) to track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of the device. In this way, the VINS combines complementary sensing capabilities. For example, the IMU can accurately track dynamic motions over short time durations, while visual data from the image source can be used to estimate the pose displacement (up to scale) between consecutive views. For several reasons, VINS have gained popularity as devices to address GPS-denied navigation.